Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 729-734, 2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
06 Jun 2016
Han Zheng1, Feitong Tan2, and Ruisheng Wang1 1Dept. of Geomatics Engineering, University of Calgary, Calgary, Alberta, T2N 1N4, Canada
2Yingcai Experimental School,University of Electronic Science and Technology of China, No.2006, Xiyuan Ave, West Hi-Tech, Chengdu, Sichuan, P.R.China
Keywords: LiDAR, Point Clouds, Local Roughness, Graph Cuts based segmentation, Pole-like object detection, DBSCAN based point clustering Abstract. Object detection and recognition from LiDAR (Light Detection And Ranging) data has been a research topic in the fields of photogrammetry and computer vision. Unlike point clouds collected in well-controlled indoor environments, point clouds in urban environments are more complex due to complexity of the real world. For example, trees sometimes close to signs or buildings, which will cause occlusions in the point clouds. Current object detection or reconstruction algorithms will have problems when recognizing objects with severe occlusions caused by trees etc. In this paper, a robust vegetation removal method and a DBSCAN based pole-like object detection method are proposed. Based on observation that major difference between vegetation and other rigid objects is their penetrability with respect to LiDAR, we introduce a local roughness measure to differentiate rigid objects from non-rigid ones (vegetation in this paper). First, a local sphere with a small radius is generated for each input point. Three principal components of the local sphere are then calculated, and a plane is determined. The roughness is obtained through calculating the standard deviation of distances from all inside points to the plane by a weighted summation of the normalized distances. The further the point to the plane, the smaller the weight is. Finally, a graph cuts based method is introduced to classify the input point sets into two groups. The data term is defined by the normalized roughness of the current point, and the smoothness term is defined by the normalized distance between the point and its nearest neighbour point. In terms of pole-like object detection, first, a uniformed 2D grid is generated through projecting all the points to the XY-plane. The seed points of the pole-like objects are obtained by determining the x and y coordinates by the centres of the highest density cells of the grid and the z coordinate by the mean height of the point sets of each object. Finally, a DBSCAN based method is introduced to obtain the rest points of each pole-like object. Experimental results show that the proposed vegetation removal method achieves state-of-the-art results from both mobile LiDAR and airborne LiDAR data. The proposed pole-like object detection approach turns out to be very efficient.
Conference paper (PDF, 1733 KB)

Citation: Zheng, H., Tan, F., and Wang, R.: POLE-LIKE OBJECT EXTRACTION FROM MOBILE LIDAR DATA, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B1, 729-734,, 2016.

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